@inproceedings{586a8bddffe9444ba6ed7cf9284efdcd,
title = "Lungbrn: A smart digital stethoscope for detecting respiratory disease using bi-resnet deep learning algorithm",
abstract = "Improving access to health care services for the medically under-served population is vital to ensure that critical illness can be addressed immediately. In the scenarios where there is a severely lacking of skilled medical staff, a basic lung sound classification through a digital stethoscope can be used to provide an immediate diagnostic for respiratory-related diseases such as chronic obstructive pulmonary. In this work, we have developed an improved bi-ResNet deep learning architecture, LungBRN, which uses STFT and wavelet feature extraction techniques to improve the accuracy compared to the state-of-The-Art works. To ensure a fair evaluation, we have adopted the official benchmark standards and the {"}train-And-Test{"} dataset splitting method stated in the ICBHI 2017 challenge. As a result, we are able to achieve a performance of 50.16\%, which is the best result in terms of accuracy compared to all participating teams from ICBHI 2017.",
keywords = "acoustic signal processing, crackles, deep learning, respiratory sounds classification, wheezes",
author = "Yi Ma and Xinzi Xu and Qing Yu and Yuhang Zhang and Yongfu Li and Jian Zhao and Guoxing Wang",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Biomedical Circuits and Systems Conference, BioCAS 2019 ; Conference date: 17-10-2019 Through 19-10-2019",
year = "2019",
month = oct,
doi = "10.1109/BIOCAS.2019.8919021",
language = "英语",
series = "BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "BioCAS 2019 - Biomedical Circuits and Systems Conference, Proceedings",
address = "美国",
}